A Machine-Learning Approach for Classifying Defects on Tree Trunks using Terrestrial LiDAR

Van-Tho Nguyen (1) , Thiéry Constant (1) , Bertrand Kerautret (2, 3, 4) , Isabelle Debled-Rennesson (4) , Francis Colin (1)

1 SILVA - SILVA
2 imagine - Extraction de Caractéristiques et Identification
3 LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
4 ADAGIO - Applying Discrete Algorithms to Genomics and Imagery

Three-dimensional data are increasingly prevalent in forestry thanks to terrestrial LiDAR. This work assesses the feasibility for an automated recognition of the type of local defects present on the bark surface. These sin-gularities are frequently external markers of inner defects affecting wood quality, and their type, size, and frequency are major components of grading rules. The proposed approach assigns previously detected abnormalities in the bark roughness to one of the defect types: branches, branch scars, epi-cormic shoots, burls, and smaller defects. Our machine learning approach is based on random forests using potential defects shape descriptors, including Hu invariant moments, dimensions, and species. The results of our experiments involving different French commercial species, oak, beech, fir, and pine showed that most defects were well classified with an average F 1 score of 0.86.

Mots clés: roundwood quality; random forests; standing tree grading

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Sylviculture, foresterie

Van-Tho Nguyen, Thiéry Constant, Bertrand Kerautret, Isabelle Debled-Rennesson, Francis Colin. A Machine-Learning Approach for Classifying Defects on Tree Trunks using Terrestrial LiDAR. Computers and Electronics in Agriculture, 2020, 171, pp.1-12. ⟨10.1016/j.compag.2020.105332⟩. ⟨hal-02931277⟩